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This paper is concerned with the application of wireless sensor network (WSN) technology to long-duration and large-scale environmental monitoring. The holy grail is a system that can be deployed and operated by domain specialists not engineers, but this remains some distance into the future. We present our views as to why this field has progressed less quickly than many envisaged it would over a decade ago. We use real examples taken from our own work in this field to illustrate the technological difficulties and challenges that are entailed in meeting end-user requirements for information gathering systems. Reliability and productivity are key concerns and influence the design choices for system hardware and software. We conclude with a discussion of long-term challenges for WSN technology in environmental monitoring and outline our vision of the future.
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INVITED
PAPER
Environmental Wireless
Sensor Networks
This paper reviews recent experiments with networks for environmental
and agricultural applications; it also provides a critical review of recent
research and considers future challenges and opportunities.
By Peter Corke, Fellow IEEE,Tim Wark, Member IEEE,Raja Jurdak, Member IEEE,
Wen Hu, Member IEEE,Philip Valencia, Member IEEE, and Darren Moore, Member IEEE
ABSTRACT |This paper is concerned with the application of
wireless sensor network (WSN) technology to long-duration
and large-scale environmental monitoring. The holy grail is a
system that can be deployed and operated by domain spe-
cialists not engineers, but this remains some distance into the
future. We present our views as to why this field has progressed
less quickly than many envisaged it would over a decade ago.
We use real examples taken from our own work in this field to
illustrate the technological difficulties and challenges that are
entailed in meeting end-user requirements for information
gathering systems. Reliability and productivity are key con-
cerns and influence the design choices for system hardware
and software. We conclude with a discussion of long-term
challenges for WSN technology in environmental monitoring
and outline our vision of the future.
KEYWORDS |Environmental monitoring; wireless sensor
network (WSN)
I. INTRODUCTION
Wireless sensor networks (WSNs) are an important tech-
nology for large-scale monitoring, providing sensor mea-
surements at high temporal and spatial resolution. The
simplest application is sample and send where measure-
ments are relayed to a base station, but WSNs can also
perform in-network processing operations such as aggre-
gation, event detection, or actuation.
The first WSN papers a decade ago [1] clearly arti-
culated the promise of the technology for a diverse range of
monitoring applications including forests, waterways,
buildings, security, and the battlefield, and how it would
transform the way we do science and business. One decade
on, it is clear that progress has not been as fast as was
predicted. Instead of smart dust sprinkled from aircraft we
have large nodes
1
connected by myriad wires to transdu-
cers. The research community is still concerned with net-
working and maximizing the lifetime of networks powered
from finite electrochemical primary cells. While many
have reported sample-and-send systems with tens of nodes
and operational durations from days to years, other fea-
tures envisaged at the outset such as event detection,
sensing and actuation, or the integration of robots and
sensor networks have not become commonplace. It seems
that the technology is still emerging.
WSN technology has followed a hype cycle [2] trig-
gered by the availability of low-cost low-power feature-rich
microcontrollers and single-chip radio transceivers. This
led to excitement, expectation, the founding of startups,
and the establishment of new conferences and journals.
Early adopters embraced the technology as end users,
willing to suffer the inconveniences of bleeding-edge tech-
nology in order to gain an advantage. Activity peaked and
the trough of disillusionment followed. The early adopters
became frustrated, the researchers felt that the early
adopters were too needy, and the expected markets did not
materialize. Then follows the slope of enlightenment when
expectations are moderated all round, the bugs are ironed
out, the real rather than supposed applications become
Manuscript received November 16, 2009; revised July 8, 2010; accepted July 8, 2010.
Date of publication October 7, 2010; date of current version October 20, 2010.
P. Corke was with the Autonomous Systems Laboratory, CSIRO ICT Centre,
Brisbane, Qld. 4064, Australia. He is now with the School of Engineering Systems,
Queensland University of Technology, Brisbane, Qld. 4001, Australia
(e-mail: peter.corke@qut.edu.au).
T. Wark,R. Jurdak,W. Hu,P. Valencia, and D. Moore are with the Autonomous
Systems Laboratory, CSIRO ICT Centre, Brisbane, Qld. 4069, Australia
(e-mail: tim.wark@csiro.au; raja.jurdak@csiro.au; wen.hu@csiro.au;
philip.valencia@csiro.au; darren.moore@csiro.au).
Digital Object Identifier: 10.1109/JPROC.2010.2068530
1
Typically 1 L in volume.
Vol. 98, No. 11, November 2010 | Proceedings of the IEEE 19030018-9219/$26.00 Ó2010 IEEE
clear, and the field grows steadily. This paper reflects our
experience on this journey for WSN technology.
AnearlyclaimwasthatWSNsareanewinstrument for
gathering data about the natural world [3] and our
collaborators who are primarily scientists have high, and
not unreasonable, expectations of something that purports
to be an instrument. In particular, they expect a high level
of system integration, performance, and productivity.
System integration means creating an end-to-end
system that delivers data to an interested user. This meant
that our mind set had to change from a narrow focus on
just the WSN component to creating an information sys-
tem. The WSN is just one small part of a complex system
that includes internet links from the WSN to a server,
databases, and web presentation tools. Each of these com-
ponents was critical for success and we underestimated
each of them. For example, the internet link from a sensor
network to the server presupposes that an internet
endpoint exists and this was not always the caseV3G
modems are ideal in principle but problematic in practice.
We naively assumed that servers within our organization
were always up, but did not know that network infra-
structure upgrades happen late at night and on weekends,
making those servers inaccessible from the outside world.
Performance has many aspects. One is reliability of the
node itself: its power source, radio links and overlying
protocols, and reliability of the application and operating
system software. Lack of performance leads to gaps in the
data record, negating the claim about high temporal pre-
cision, and incorrect data (due to all manner of root
causes) lead to a lack of trust and confidence in the system.
Accuracy and calibration are critically importantVit is not
enough that the network returns numbers to a central
database which is the engineers concern; the numbers
must accurately reflect the state of the environment [4].
Sensor calibration and detecting stuck values from broken
transducers are very important.
Productivity has two aspects. Most important is how
well it assists the end user to do their science or business,
and is largely related to human interface design and the
performance of the underlying database and presentation
software. Databases and web presentation tools are simple
student projects when they have to deal with trivial
amounts of data, but as the data volume grows perfor-
mance falls dramatically and will frustrate the end user
and lower their productivity. For developers and main-
tainers of WSNs, productivity is about reducing the total
cost involved in a sensor network over its lifetimeV
analogous to total cost of ownership (TCO) for a computer
system. Costs that must be accounted for include planning,
node hardware, deployment, troubleshooting, and main-
tenance. Only semiconductors follow Moore’s law; the
other costs follow a learning curve and are already mature.
Applications of sensor networks are very diverse but we
use as case studies some of the many applications that we
have tackled which are outdoor, and concerned in some way
with the natural environment and/or agriculture. Our choice
of applications was dictated by interest from our collabora-
tors in those fields and the national challenges facing our
country such as land degradation, water shortage, and
climate change. Our collaborators have high expectations
and they have been an important part of our learning.
These applications share common challenges such as
robustness to unpredictable events and harsh outdoor
conditions, and goals such as network longevity. In most of
our deployments, delays in data delivery were not a major
issue, neither was providing absolute guarantees on data
delivery. This has guided our design choices and dis-
tinguishes our focus on outdoor network deployments
from other sensor network application classes, such as
industrial automation or indoor tracking.
In this paper, we attempt to explain the slow progress
of the field by exploring our own progress, the pitfalls we
experienced, and the lessons we learned. The next section
of the paper discusses in chronological order a number
applications, the technological challenges that they pre-
sented, and a summary of the technologies we developed
in response to these challenges. Section III describes our
current state-of-the-art end-to-end system. In Section IV,
wediscusschallengesforthefieldsuchasvaluepropo-
sition and alternative technologies, and in Section V, we
take stock and discuss what can be done with the tech-
nology now and into the future.
II. APPLICATIONS
In this section, we discuss the major sensor network ap-
plications that we created over the past six years (Table 1),
the unique technical challenges they posed, and the lessons
we learned from them. The applications all share a common
theme: understanding the natural and agricultural envir-
onments in response to major challenges faced by Australia.
The applications include microclimate monitoring for
farms and rain forests, water-quality monitoring, and cattle
monitoring and control. Two applications involve actuation
in addition to sensing: cattle are actuated by applied
stimuli, and a robotic boat is actuated to perform sampling.
Each application presented different challenges which
included mobility, actuation, energy, and intermittent
connectivity. Our design choices and technologies were
responses to these challenges and have evolved over time
but the rate of change is slowing and, after six years, we are
nowatastagewherethecoretechnologyisreliableand
configurable enough that it is has been deployed, unaided,
by domain scientists.
A. Cattle Monitoring
We developed a network at a research farm over
500 km from our laboratory. The project has had several
phases and technology generations, and it has been the
primary driver of our technology development. The first
phase involved recording the positions of cattle over time,
Corke et al.: Environmental Wireless Sensor Networks
1904 Proceedings of the IEEE |Vol.98,No.11,November2010
and also soil moisture at various points in a paddock. Soil
moisture is an important indicator of how quickly pasture
will grow and therefore important for planning stocking
rates (number of animals per unit area).
1) Challenges: Information from static and mobile nodes
was to be relayed to a base station and then over the in-
ternet to a remote server. We later added nodes with cam-
eras that periodically transmitted images of key locations
such as water troughs. This was our first deployment that
included both mobile and static nodes and this raised new
challenges for routing and network topology maintenance.
2) Technology: From our early experiments with Mote
hardware we saw the need to develop a device with
improved radio range, solar power capability (for a country
where solar insolation is of the order of 20 MJ/m2/day),
mechanical and electrical robustness, and ease of inter-
facing to transducers. This became the Fleck series of
nodes shown in Fig. 2.
The Fleck-1 used the Atmega 128 processor, with
128 KB of program flash memory, 4 KB of RAM,
and a stream-based Nordic nRF903 radio transceiver at
433 MHz which provided a 72-kb/s channel and a range of
500 m using a quarter-wave antenna. The board was
60 60 mm2in size and includes power supply, solar
charging circuit, and sensing for on-board temperature,
battery voltage, and charging current.
In retrospect the most inspired part of the system design
was the simplest. A screw terminal block on the Fleck made
it very easy to install. Battery, solar cell, serial, and analog
and digital transducers could be connected using just a
screwdriverVno expansion board was required. However,
for more complex interfacing, a simple microprocessor
pinout expansion bus was provided and an expansion board
for the soil moisture transducers was developed.
The nodes carried by the animals were the Fleck-2
which was specialized for animal tracking applications. It
has the functionality of the Fleck-1 with additional on-
board global positioning system (GPS), three-axis magne-
tometer (electronic compass), three-axis accelerometer,
and a multimedia card (MMC) socket for local bulk data
storage as a safeguard against intermittent network con-
nection or the case where data rate exceeds network
capacity. The Fleck-2 nodes were built into collars that
were worn by the cattle [Fig. 1 (left)].
For image processing the Atmega processor lacks mem-
ory and computational power. Unlike the Cyclops [5], we
Table 1 Major Sensor Network Deployments. The Deployments Marked With Are Discussed in Section II in Detail. More Details Can Be Found at
www.sensornets.csiro.au
Fig. 1. Sensor nodes in the farm deployment: (left) cow collars,
(right)second generationenvironmental housingwith solar cell on top.
Corke et al.: Environmental Wireless Sensor Networks
Vol. 98, No. 11, November 2010 | Proceedings of the IEEE 1905
chose to implement the camera as an expansion board with
a Texas Instruments 32-b 150-MHz digital signal processor
(DSP) with 1 MB of SRAM connected to a 640 480 color
image sensor. The DSP performs image capture, buffering,
and processing and sends the image in small blocks over the
serial peripheral interface (SPI) bus to the Fleck for
transmission over the radio network [6].
The nodes were programmed under TinyOSand and
used an inhouse developed self-organizing time-division
multiple-access scheme (ZTDMA) which cooperated with
the Deluge [7] protocol for over-the-air reprogramming.
The BMAC [8] protocol became part of TinyOS after we
starteddevelopmentandwedidnotadoptitfortworea-
sons.First,wewereconvincedtherewasenoughpower
from the sun to not warrant duty-cycled radio commu-
nications. Second, we had forked the TinyOS code tree to
support our Fleck platform and BMAC was integrated into
a different version of TinyOS.
3) Lessons Learned: ZTDMA had very poor end-to-end
delivery rates since it used network-wide flooding to
achieve multihop communication, and therefore scaled
poorly with network size. This became increasingly prob-
lematic as nodes were added, particularly the camera
nodes which periodically transmitted large bursts of data.
The rapid development of TinyOS at that time made it
difficult to add desirable new features into our Fleck-1 port
of TinyOS, partially negating the benefits of a common
software platform.
Software became increasingly complex and took longer
to debug which, combined with the remote location, made
our productivity low and led to tension with our collabo-
rator. Managing a remote network was hard, and we had
no easy way of accessing the state of any of the nodes.
Abundant solar power and a simplistic battery charging
circuit led to overcharging of the batteries which greatly
reduced their lifetime.
We had significant problems with the environmental
housings, a prosaic but critical part of a sensor network
system. Our first generation housings were standard gray
plastic electrical boxes and the quarter-wave 433-MHz
antennas were externally mounted. Box penetrations for
transducers, solar power, and radio frequency (RF) were
potential sources of water and insect ingress, but the bigger
problem was the time taken to assemble each unitVnearly
half a day. The bigger form factor of the Fleck-2, 60
120 mm2was problematic for mounting in the animal
collars and there was, in retrospect, no clear advantage in
cost or reliability of the single board solution. In fact the
small number that was manufactured led to higher unit costs.
B. Ground Water Quality Monitoring
Deployment C was a relatively small network, nine
nodes, located 2000 km from our lab. Its purpose was to
monitor the salinity, water table level, and water extrac-
tion rate at a number of bores within the Burdekin irri-
gated sugar cane growing district. This is a coastal region
and over extraction of water leads to saltwater intrusion
into the aquifer. The area we monitored was approximately
23km
2.
1) Challenges: The WSN had to operate unattended, and
compared to previous sensornet deployments the network
was very sparse with very long wireless transmission
ranges (with average link length over 800 m). One simpli-
fication was that many nodes could be mains powered
(since they were colocated with pumps).
2) Technology: The Fleck-3 series usedaNordicnRF905
radio which had a more sensitive receiver giving longer
radio range. It has an inbuilt proprietary media access
control (MAC) that supports packets up to 32 B and re-
quired only an SPI-bus connection to the processor. Like
its predecessor, this transceiver did not provide received
signal strength indication (RSSI) or link quality (LQ) in-
formation. It also used a different modulation scheme
making it incompatible with the Fleck-1 and -2 radio, and
wealsomadethedecisiontomovefrom433to915MHz
primarily for the smaller antenna size (which could then
be placed inside the enclosure). This was the period when
the rest of the WSN community was moving en masse to
2.4 GHz. The node also included an improved solar
charger that allowed the solar cells to be disconnected
from the battery, under software control, to prevent over
charging. Developments in microelectronics also allowed
the board to be slightly smaller, 50 60 mm2.
We used the TinyOS operating system and imple-
mented the MintRoute
2
network protocol which uses a
shortest-path-first algorithm to route packets to the base
station on the basis of a definable routing metric. We chose
bidirectional Expected number of Transmissions (ETX) as
our routing metric which works well with the radio chip
we selected. We also use hop-to-hop retransmission to
increase end-to-end delivery rates.
3) Lessons Learned: The technology was transitional: our
last system to use TinyOS
3
and the Surge protocol and our
first to use the new Fleck-3 node. We deployed no other
network using this technology combination. The impor-
tance of protocols became very clear, and this system was
able to achieve more than 95% end-to-end delivery rates
in deployment when the individual LQ was higher than
15%. We conducted a radio survey to determine achiev-
able communications distances in the environment, but
we did this when the fields were bare. We neglected to
account for the sugar cane which is up to 4.5 m tall when
fully grown and interferes with line-of-sight wireless
communication [9].
2
TinyOS multihop routing, http://www.tinyos.net/tinyos-1.x/doc/
multihop/multihop_routing.html.
3
Although FOS was prototyped the project time lines were such that
we considered it prudent to use TinyOS.
Corke et al.: Environmental Wireless Sensor Networks
1906 Proceedings of the IEEE |Vol.98,No.11,November2010
Thenetworkwasdeployedin2006andoperatedfor
1.5 years, delivered more than 1 million water quality
readings, and required only two maintenance visits. One
visit was to repair a number of nodes damaged in a violent
electrical storm.
C. Virtual Fencing
A new group of large-scale and remotely operated de-
ployments was looming that included cattle control. The
lessons learned so far stressed the need for better found-
ational technology. This led us to develop our own oper-
ating system and a new node with a better radio but which
was incompatible with the Fleck-1 and Fleck-2. Deploy-
ment D, at our laboratory, was a testbed to shakedown this
Bnew technology.[
We also developed a moulded plastic case that was
quick to assemble and had the antenna inside the enclo-
sure [Figs. 1(right) and 5]. The new unit could be
assembled in just 15 min.
1) Challenges: Key requirements included the ability for
access to status and control of remote nodes, and this was
particularly important to meet the ethics requirements for
the virtual fencing [10] experiments.
2) Technology: The new Fleck-3 was incompatible with
the Fleck-2 nodes used in the cow collars. Rather than
redesign a Fleck-3 variant for animal tracking, we chose
this time to exploit the expansion bus on the Fleck.
Expansion boards are stackable [Fig. 2(e)] and the whole
assemblycouldbeboltedtogetherPC-104styleleadingto
systems that were extremely robust mechanically and
electrically. The Fleck-2 functionality now fitted on two
50 50 mm2expansion board: inertial sensing (accel-
erometers, gyroscopes, and compass), and GPS/MMC
combination.
The growing complexity of our deployments led us to
develop our own operating system. A sensor network
operating system must abstract underlying hardware,
facilitate resources sharing, and be power conscious and
sleep whenever possible. TinyOS [11] was the first open-
source operating system for sensor nodes and is event
based. However, we and others have found that event-
driven code is difficult to write, understand, maintain, and
debug [12]. It does not scale well with program size, lead-
ing to difficulty in developing and maintaining large appli-
cations [13]Vit requires that logically blocking sequences
be written in a state-machine style [14]. The result is that
the control flow for a single conceptual task and its state
are split across several language procedures, effectively
discarding language scoping features [15].
Our Fleck operating system (FOS) sits at the Bsweet
spot[identified by Adya [15]. FOS provides a priority-
based nonpreemptive (cooperative) threading envi-
ronment. This simple concurrency model means that
semaphores are not required. The scheduler is responsible
for CPU power management and enters the lowest mode
consistent with thread resource requirements. Other ap-
proaches to threads on sensor nodes include preemptive
threading [16] and protothreads [17]. Interestingly TinyOS
itself eventually supported the threading model [18] and
justified it in terms of ease of use and significantly greater
expressivity compared to the event-based model.
FOS provides uniform access to underlying resources via
a POSIX-like application programmer interface (API) with
blocking read and write primitives. FOS supports the many
transducer interface boards we have developed. Time-
critical operations such as analog data sampling or high-
speed timers are handled by interrupt-level callbacks. A
virtualized timer based on an event-time queue is provided
for non time-critical delays. The kernel is a relatively small
piece of software, around 12 000 lines of C code.
The most well-known and cited disadvantages of
threads are that each thread must reserve its own stack
space that cannot be shared and, because it is difficult to
know in advance how much stack space a thread needs, the
stack is typically overprovisioned. We addressed these
concerns in several ways. We developed a static analyzer
for estimating required stack sizes for each thread which
eliminates wastage. We use an interrupt stack so that we
do not have to allocate space for interrupt handlers on
every thread stack. Finally, the kernel checks sentinel bytes
in the stack on each system call and invokes a panic if a
stack overflow has occurred. In practice, the memory
Fig. 2. Evolution of CSIRO WSN mote platforms. (a) Mica Mote. (b) Fleck 1c. (c) Fleck 2. (d) Fleck 3. (e) Fleck 3b stack.
Corke et al.: Environmental Wireless Sensor Networks
Vol. 98, No. 11, November 2010 | Proceedings of the IEEE 1907
required for thread stacks has not proved to be a limitation,
and applications with ten or more threads are routinely
created.
Other support tools include a wireless boot loader that
uses our remote procedure call (RPC) protocol [19] for
command and status, and a negative-acknowledgment-
based protocol for efficient reloading of multiple nodes
simultaneously over the radio. Software failures return
control to the boot loader allowing remote intervention or
restart. A postmortem memory dump analyzer provides
complete state and symbolic debugging information from
memory images uploaded from nodes that have failed in
the field.
FOS needed a routing protocol and we chose diffusion
routing to meet the mobility requirements [20]. In dif-
fusion a node (source) periodically broadcasts/advertises
interests. Other nodes (destination) can subscribe to the
interests and the sensed data will be routed from source to
the destination via the reverse path.
As an alternative to programming the node we can
consider that the node exports a predefined set of services
which a client program can access. The services are ac-
cessed using RPCs. Other RPC systems have been pro-
posed for sensor networks [21]–[23] but our system is now
the mainstay of many long-lived deployments and has
proven invaluable for diagnostics and network retasking.
The exported services are code segments, called
actions, which closely resemble C-language functions. An
RPC generator takes a set of actions and creates two sets of
output files: the server-side C-code to be included on the
node, and the client-side Python class. Our tiny RPC sys-
temusesastatelessprotocol,andtheRPCserverrunsasa
separate thread on the node. Every node includes a set of
standard services including reading or writing RAM or
electrically eraseable programmable read only memory
(EEPROM), reading transducers or battery condition, and
rebooting.
An application, which is a WSN client written in
Python, invokes a method call on an RPC object which
causes the arguments to be marshaled, and sent via a
gateway node to the WSN. There the action is executed and
the return values marshaled and returned to the ap-
plication. In the case that the function call is broadcast
rather than unicast the method call returns a list of return
values. Thus, a client program running on a host computer
can seamlessly access services on one or more sensor nodes.
Using this facility, we have developed the network
equivalent of a Bsymbolic debugger,[a keyboard-interactive
tool which allows node state to be examined or altered [19].
If the node application’s symbol table is available we can
address remote node memory symbolically. Our experience
validates the experience of others, for example, [24],
regarding the importance and utility of remote monitoring
and control mechanisms.
As the number and complexity of deployments grew we
had a problem with the number of different message
formats. This deployment (E), for example, was highly
heterogeneous with many different kinds of transducers
such as soil moisture, battery voltage, and GPS position, as
well as virtual sensors such as node and network per-
formance metrics. We refactored several applications and
created a general message format called tagged data
format (TDF).
TDF is a self-describing schema for any data transmit-
ted within the network, including RPC call and return
frames. When a node sends data, it is packed into the
payload of a message and tagged, prepended by a unique
byte value, to identify the type of data and implicitly its
length in bytes. This enables the creation of generic Bback-
end[tools that can parse the message payloads without
knowledge of the application that created them.
3) Actuation: BVirtual fencing[(VF) [10] not only re-
quires sensing of position and velocity information, but
also actuation which in this case is the application of audio
and mild-electrical stimuli to the animals. While applying
stimuli is technically trivial, an independent ethics over-
sight committee required that it be applied ethically. With
a nonzero probability that software bugs could breach the
ethics commitment, we designed our stimulation hardware
with a dedicated low-level controller that enforced our
ethics constraints. Additionally, we designed the applica-
tion to deactivate the VF logic should a communications
link back to the base be lost. This meant that we could
monitor the animals in real-time, deactivate collars if
necessary via RPC, and know that when we could not
contact the collars, that no stimuli was being applied. As
the confidence in the logic matured, the need for a con-
tinuous communications link was relaxed, however the
embedded ethics-enforcing logic on the hardware has re-
mained. We have successfully demonstrated significant
reduction of grazing on exclusion zones designated using
our BVF[algorithm on hundreds of cattle not previously
exposed to the technology [25].
4) In-Network Processing: The cattle monitoring work
also gave us the opportunity to investigate some challenges
around in-network processing of high sample-rate GPS
data. The cattle nodes have a fixed-sized memory buffer to
which position data are added at a constant rate, and from
which data are downloaded at a nonconstant rate when
they come into contact with static sensor nodes. We have
developed a novel algorithm that performs online summa-
rization of position data within the buffer, where the
algorithm naturally accommodates data input and output
rate mismatch, and also provides a delay-tolerant approach
to data transport [26]. The algorithm has been extensively
tested in a large-scale long-duration cattle monitoring and
control application.
Data summarization and aggregation has been a grow-
ing area of focus within the sensor networks community
[27], [28]. For many applications, the Bsample-and-send[
Corke et al.: Environmental Wireless Sensor Networks
1908 Proceedings of the IEEE |Vol.98,No.11,November2010
paradigm is not the right one, especially in applications
which require periods of streaming high-fidelity data and
periods where no data are required. Growth in solid-state
memory capacity and shrinking costs mean that availability
of local storage space is increasingly not a challenge [29].
The key research opportunities here revolve around the
most effective ways to compress data which allow for effi-
cient search and communication of the most valuable in-
formation contained within.
5) Lessons Learned: Thedecisiontochangebothhard-
ware and software was correct in retrospect, even if it was
painful at the time. From a hardware perspective, the pain
wasinobsoletingallthenodeswehadpreviouslybuiltata
significant cost, and this was difficult to Bsell[to our
partners. Writing code on a developing operating system
was difficult for all concerned, but we did achieve the goal
of a programming environment that was more convenient
for applications developers. FOS was rapidly adopted and
led to a huge jump in productivityVwe found that students
and visitors were up and running in hours. We routinely
created complex applications with ten or more threads.
This is counter to the choices made by other groups and
advocated in the literature. In our experience, memory
limits turned out not to be an issue, and in retrospect the
tradeoff made in early WSN research where memory was
saved at the expense of increased program complexity was
perhaps not the right one.
Increased productivity and several parallel projects
meant that message formats got out of control, they were
not defined by FOS, and each project defined its own
message format for data and for commands. This reached
crisis point and was refactored into the self-describing data
format TDF and a tiny remote procedure call system.
These tools were quickly adopted by the team and led to
another lift in productivity and in our ambitionVwe could
contemplate building much more complex systems be-
cause the foundations and the debugging tools were strong.
The downside of adopting custom hardware, a radio
with no LQI or RSSI and a small packet size, and custom
software was that we cut ourselves off from the main-
stream of sensor network development. We made do with
Bgood enough[networking algorithms and devoted our
effort to system integration. One interpretation of this is
that the gains from protocol development are diminishing,
and that systems issues now dominate. In fact our code
base has a kernel with 12 000 lines of code (LOC) and the
Python support tools and utilities have 23 000 (LOC).
4
D. Rainforest Monitoring
Deployment F was part of a major initiative to provide
reliable, long-term monitoring of rainforest ecosystems.
Our target was a rainforest area in South-East Queensland
(Springbrook, Australia) which had a high priority for
monitoring the restoration of biodiversity. The first phase
of the project was to develop a better understanding of the
challenges in deploying long-term, low-power WSNs in
rainforest environments [30]Vthe engineering testbed.
These environments are typically characterized by areas
with very limited solar energy with adverse and dynamic
radio environments. In order to develop the network and
energy management protocols required for robust and re-
liable performance of long-term, rainforest networks, we
had to first quantify the performance of current WSN
technology under these conditions.
1) Challenges: A well-recognized constraint on sensor
networks, identified in the earliest research, has been
energy resources. Sensor nodes are expected to be de-
ployed in environments away from the energy grid and
must either hold sufficient energy resources to last the
required lifetime, or have their energy store replaced
manually or replenished continuously with energy har-
vested from the surrounding environment. Sensor net-
works nodes comprise a number of core components such
as a microcontroller, radio, flash memory, transducers, and
other peripherals. Each of these components can be in one
of a number of states each of which has different power
consumption. Fig. 3 illustrates the energy consumption
rates of a node as a function of the states of its core com-
ponents. Based on these data and given a typical radio duty
cycle of 5%, and transducer sampling/sending rate of
around 5 min, we can estimate that node’s average current
consumption is around 2 mA.
2) Technology: Our current approach to managing bat-
tery storage is to combine both rechargeable and non-
rechargeable batteries in each device. In the default mode
of operation, all energy for the device will come from three
rechargeable 1.2-V 2700-mAh NiMH batteries working in
combination with monocrystalline solar panels capable of
supplying up to 300 mA of current. In the event no further
energy is harvested for long periods, the system will switch
to the nonrechargeable (Alkaline) energy supply when the
rechargeable battery voltage falls below a threshold, and
switches back whenever this voltage rises again.
Fig. 4 shows the results of an experiment over two
weeks with nodes deployed in open and covered areas. All
nodes are deployed in the open with the exception of nodes
B20,[BLog-runner,[and B19,[which are placed in fo-
rested areas. Whereas nodes in the open typically harvest
over 10 kJ/day, the covered rainforest nodes harvest as
little as 100 J on averageVonly 1% of the energy of nodes
in the open. A node drawing an average of 2 mA requires
600 J/day so sustainable operation would require an aver-
age current consumption of less than 0.33 mA.
The engineering testbed deployment showed that the
links were highly dynamic and asymmetric. To meet high
end-to-end delivery requirements we implemented a
LQ-based routing protocol for FOS which became the
4
Measured usinghttp://www.dwheeler.com/sloccount by D. Wheeler.
Corke et al.: Environmental Wireless Sensor Networks
Vol. 98, No. 11, November 2010 | Proceedings of the IEEE 1909
protocol used across all future deployments, and was
retrofitted to deployment E. LQ is similar to the collection
tree protocol (CTP) in TinyOS [28] and uses ETX as
routing metrics and takes bidirectional LQ into account.
However, hardware LQ indicators, such as RSSI and LQI,
are not available from the Fleck-3’s transceiver. Instead
packet reception rates (PRRs) are estimated by snooping
neighbor traffic. Snooping a packet from a neighbor has the
same energy cost as receiving a packet but is better than
the alternative of using node goodput to estimate PRRs. The
estimation of LQ to sibling and child nodes based only on
replies to infrequent beacon messages will not be as fresh as
the estimate obtained by snooping neighbor traffic. For a
difficult rainforest communication environment, we trade
off energy for the robustness of communications. As a
result, LQ achieved more than 99% end-to-end delivery
rates when the network was connected.
To help meet the power budget, we implemented a
low-power MAC protocol based on low-power listening
(LPL) [8]. In LPL, nodes wake up periodically (every
57 ms) for a short period (3 ms) to check for commu-
nication activities and attempt to receive messages.
Consequently, a source node needs to transmit a long
Bpreamble[to wake up destination node before transmit-
ting a message and this incurs a slightly higher cost for the
sending node. Because traffic is typically low in a habitat
monitoring sensor network, messages are sent every 5 min;
nodes using LPL can sleep most of the time and conserve
energy.
The nodes used the now standard Fleck-3 boards and
environmentalhousingsasshowninFigs.5and6.Acus-
tom expansion board was built to interface to the many
transducers: wind speed and direction, soil moisture, leaf
wetness, temperature, and relative humidity.
We also started the next generation of video and audio
interface. The system developed for deployment B used a
DSP that had a poor software development environment.
We redesigned this to use an Analog Devices BlackFin
processor and we experimented with both uCLinux and
the Visual DSP++ environment [6] before settling on the
latter as our development environment of choice. The new
interface has a mega-pixel color image sensor, two audio
Fig. 3. (a) Breakdown of total current consumption by CPU and radio(100% duty cycle) for different states. (b) Breakdown of transducer energy
consumption per sample. (a) CPU and radio. (b) Sensors.
Fig. 5. Inside the second generation environmental housing showing
the seals.
Fig. 4. Average daily energy harvested by each node.
Corke et al.: Environmental Wireless Sensor Networks
1910 Proceedings of the IEEE |Vol.98,No.11,November2010
channels and interfaces to passive infra red (PIR)
transducers for triggering. The applications include
event-based and periodic image capture or sound record-
ing. Some initial work into classification of vocalizing
species has been undertaken.
3) Lessons Learned: We learned that radio propagation
through dense and wet forest is poor, and that the decision
tomovefrom433to900MHzmaynothavebeentheright
one. The LQ protocol did work very well despite the
limitations of the radio transceiver. Results from experi-
ments [30] showed that throughput for nodes in open area
ranged from >99% for one-hop nodes down to 80% for
nodesuptofourhopsfromthesink.Inthecaseofforest
nodes, throughput ranged from 95% to less than 20% in
the worst case periods. In particular, we found that many
links of forest nodes would completely breakdown during
and after heavy rain events.
This was also the deployment where we learned that
available solar power is not always enough to power a
nodeVespecially when a large suite of transducers must be
powered. This does however raise an important question
about an unwritten assumption for WSNsVthat nodes are
deployed and never revisited. In practice, our rainforest
nodes are visited every few months to remove leaves,
sticks, and insect nests from the transducers. It would
therefore be quite feasible to replace the battery on each
visit, eliminating the complexity associated with energy
management discussed above.
E. Lake Water Quality Monitoring
The purpose of this deployment was to measure vertical
temperature profile at multiple points on a large water
storage that provides most of the drinking water for the city
of Brisbane, Australia. The data, from a string of temperature
transducers at depths from 1 to 6 m at 1-m intervals, provide
information about water mixing within the lake which can
be used to predict the development of algal blooms.
1) Challenges: Low-power wireless communications over
water proved to be a challenge due to multipathing (radio
waves reflected from the water surface destructively inter-
fere with waves traveling directly) and the nonvertical
orientation of the antennas in windy or wavy conditions
(effective channel gain is reduced if antennas are not
parallel). Interfacing a robotic boat to the static sensor
nodes was another challenge.
2) Technology: The network comprises floating sensor
nodes which contain the now standard platform of a
Fleck-3, FOS, and a custom expansion board for the one-
wiretemperaturetransducerstring.Thenodeismounted
on an anchored float [see Fig. 7(left)], along with a solar
cell and a high-gain (6 dB) whip antenna mounted atop a
1.5-m mast. Bright, but low-power, strobes are activated
at night to prevent collision with other water craft.
The most novel element in this network [Fig. 7(right)] is
a 14-ft twin-hull solar-powered robotic boat. Navigation is by
GPS and depth sounder, and a scanning laser range-finder
mounted high and looking forward detects obstacles. The
onboard navigation computer communicates via a serial port
with a Fleck which serves as a gateway to the floating sensor
network. Specific RPC calls received by this Fleck are
forwarded to the navigation computer for execution, and the
return status is returned to the RPC caller [31].
We use the robot to crosscheck the calibration of
deployed nodes using its own higher quality temperature
transducer which can be set to different depths, and also to
measure temperature along a transect between nodes. This
architecture allows us to achieve high-rate measurements in
parallel with automated measurements across space, and to
access both high- and low-precision transducers. In the
future, we plan for anomalous events detected by the
network to be automatically investigated by the robotic boat.
Robots have been used previously to deploy and repair
sensor networks [32], localize nodes post deployment [33],
Fig. 6. A microclimate node deployed in the Springbrook rainforest.
Fig. 7. Lake deployment. Robotic boat in the foreground and
a floating node in the background.
Corke et al.: Environmental Wireless Sensor Networks
Vol. 98, No. 11, November 2010 | Proceedings of the IEEE 1911
[34], and to collect data from nodes and physically transort
it back to base using underwater robots [35], or fixed-wing
unmanned aerial vehicle (UAV) [36].
3) Lessons Learned: TheRPCframeworkprovedtobea
very effective way to remotely command the robotic boat,
as well as to determine the status of the floating nodes. It is
interesting to note the increasing level of abstraction with
which we consider the network over the six-year period.
We started with TinyOS where continuations and context
must be managed by the user and then to FOS where they
are managed by the operating system. We then moved
from programming in NesC/C to application-specific
virtual machines [37], special languages [38], and eventu-
ally to a version of Java [39] that ran on a node and
supported classes, threads, and exceptions. We then
moved away from node-level programming entirely by
using remote-procedure calls. Primitives within the nodes
could be composed using programs written in Python
running on a host computer anywhere on the internet.
We also learned that communication across water is
difficult with links typically less than 500 m, compared to
thereliable1000mweexperience at the farm. Preliminary
trials with nodes running at 433 MHz show much better
performance across water, and combined with the
experience from deployment F argue that sensor nodes
for environmental sensor networks should perhaps con-
sider radios operating in the very high frequency (VHF;
30–300 MHz) band.
F. Summary
In retrospect the factors most critical to our success
across these applications have been:
1) choosing a radio transceiver that gave low-power
long-range links;
2) a robust MAC protocol based on bidirectional LQ
estimation;
3) easy network reconfiguration based on RPC;
4) simple uniform data representation (TDF);
5) early adoption of solar power for sensor net-
works [40].
In a narrow sense, none of these are contributions to the
field of sensor networks but individually and in combina-
tion they are critical to the technology tackling long-term
real-world problems. Our developments were based on
results already in the literature but with significant effort
in implementation to ensure their reliability and usability.
III. END-TO-END SOFTWARE SOLUTION
Our end-to-end WSN software solution has evolved to
conveniently present data to the end user (the ultimate
purpose of a WSN) and to automate recurring tasks that
are common to all deployments. It is the culmination of
our numerous real-world deployment experiences, mainly
outdoor solar-powered deployments, and substantial soft-
ware development effort. It comprises a comprehensive
suiteofback-endtoolsandagenericsensor-node
application ToSense.
The architecture of our end-to-end system is illustrated
in Fig. 8 for the case of deployment E. Our back-end
toolset consists of software that manages sensor data at the
network gateway level, a central relational database, a web
portal (Fig. 9) for data visualization, and a collection of
Python utilities to remotely control and monitor deployed
nodes and networks. Automated monitoring utilities de-
tect hardware failures and alert relevant personnel. Data
retrieval and visualization tools allow slow degradations in
transducer performance or battery capacity to be detected
and rectified. Data can be queried directly from nodes [41]
but our focus was on providing the greatest flexibility
across a wide range of application domains, and this was
the basis for our decision to use a central database for data
management.
ToSense runs under FOS, uses the low-power MAC and
LQ routing, and provides functionality for sensor manage-
ment (add, remove, retask, etc.), as well as interrogation of
node health using remote procedure calls. This is another
example of increasing functional abstraction for WSNs and
is enabled by the underlying software tools. The ability to
query the health of individual nodesiscrucialtodiscover
Fig. 8. Data flow in and out of the WSN and interaction with the
back-end Python tools.
Corke et al.: Environmental Wireless Sensor Networks
1912 Proceedings of the IEEE | Vol. 98, No. 11, November 2010
the cause of data irregularity. Sensor configuration data are
stored in the Fleck’s external flash memory, which is or-
ganizedwiththeCoffeefilesystem[42].ToSense supports
over-the-air multihop code replication for remote software
upgrades. A dedicated thread manages the sampling of
transducers at the correct times, a solar charge manage-
ment thread prevents overcharging, and a watchdog thread
restarts the node in the event of software failure.
IV. DISCUSSION
From the outset, the promise of WSNs has been to develop
the next generation of distributed sensing technologyV
free of the need for external infrastructure such as cell
towers or satellites. This in turn increased expectation
around a new frontierVlarge-scale, pervasive, environ-
mental sensing which would transform the way we observe
and sustainably exploit the natural world. More than a
decade on from this original vision however, we are far
from seeing widespread use of large-scale sensor networks
becoming a reality. Networks are typically relatively small
in size (G30 nodes) and/or only deployed over short pe-
riods (days to months) of time. Network nodes are almost
exclusively programmed by experienced software engi-
neers and maintenance costs required to sustain contin-
uous operation of networks are usually significant and
usually borne by the sensor network researchers.
In the remainder of this section, we give some
insight from our own firsthand experiences, described in
Section II, as to why the barrier for widespread adoption of
this technology is still so high. Based on learnings from our
own design, development, and deployment experiences,
we identify some of the key technical challenges of this
field which remain to be solved. We also consider some of
the future challenges around value-proposition and alter-
native technologies.
A. Ongoing Technical Challenges
The field of sensor networks has become very popular in
many ways due to the breadth and depth of its technical
challenges. In moving to an environment free of fixed
communications infrastructure, and introducing signi-
ficant constraints around energy and computational
resources, much of the standard thinking around commu-
nications, networking, operating systems, hardware plat-
forms, and sensing has had the opportunity to be rethought
from first principles. While this has undoubtedly raised a
compelling new set of computer science and engineering
research questions, many of the technical advances in the
field are arguably reaching the point of diminishing returns.
In the majority of sensor-network applications, radio
clearly dominates the energy consumption. As such, much
of the community has focussed on ways to reduce the radio
duty cycle to save energy. As it stands, duty cycling at the
communications link layer has plateaued at around 1%–5%
[8] for most practical deployment scenarios and is unlikely
to improve significantly with current radio technology.
Reliability of data communications is also an important
problem for WSN. As a number of our deployments
showed, the variability in conditions in many environ-
mental areas (e.g., foliage, rain, humidity) means that
communication LQ between nodes is highly dynamic and
unpredictable. Given the constraints around radio output
power and fixed antennas used with most nodes, gua-
ranteeing the delivery of data over multiple node hops is
extremely difficult, with low data throughputs expected for
nodes with long hop counts to a sink.
Energy has been and remains a challenge for sensor
network deployments. The energy state of a node places a
constraint on the performance that a node can deliver. A
node’s energy state reflects its stored battery energy, actual
and predicted harvested energy (through solar and other
sources), and its energy load. Progress in battery technol-
ogy has been much slower than increases in processing and
communication rates, which emphasizes the importance of
energy-efficient operation. We have observed through de-
ployments that the amount of harvestable energy from solar
current is highly dependent on geographic location, season,
and deployment environment. For instance, the canopy
cover in the rainforest dramatically reduced the harvestable
solar energy compared to our lake deployment, where
summer solar energy far exceeds the nodes’ daily energy
usage. Solar prediction models that account for these varia-
tions are key to forecasting the node’s energy state.
The lack of resources for building and deploying net-
works in the order of thousands of nodes means that
practical issues around scale are yet to be fully explored. In
the case of typical collection tree protocols back to a single
gateway, it is clear that these protocols cannot scale by
orders of magnitude given the current capacity of network
links. Whereas a Bsample-and-send[paradigm may be suit-
able in some applications, there are increasingly com-
pelling opportunities around nodes taking on more
Fig. 9. Web view of a deployment.
Corke et al.: Environmental Wireless Sensor Networks
Vol. 98, No. 11, November 2010 | Proceedings of the IEEE 1913
adaptive Bevent-driven[rolessuchasrespondingto
queries from users or significant changes in environmental
phenomena [43]. This is evident from our deployments
such as cattle monitoring where a sample and send of raw
position or movement data is not feasibleVinstead the
network must be able to send back summary information,
either when requested or when the opportunity arises.
This also opens the question of where Bintelligence[
should reside within these networks as the research com-
munity seeks to expand the capabilities from simple, dis-
tributed sensing devices to distributed, intelligent
networks. Increasing the computational load at the node
to extract information from data is appealing from an
energy perspective in that it greatly reduces the commu-
nication cost which is dominated by radio budgets. It does
howeverplaceahigherpriorityontheneedforhigh-
quality links to ensure that information can be returned
with minimum latency. Almost certainly the appropriate
decision on these kinds of questions will be determined by
the specifics of each application.
B. Cost Benefit
For any emerging technology, economic drivers and
cost benefit are pivotal issues which could have a dramatic
effect on its market growth. Sensor networks face a number
of challenges in this regard. The field arguably emerged due
to the commoditization of cheap, low-power, single-chip
microcontrollers and radios. These components emerged
due to the rapid growth of global industries such as cell
phones, wireless remotes, and car locks. Likewise, battery
technology, while not following Moore’s law, has still seen
significant increases in energy density and reduction in
price due to the increased demand for portable electronic
devices.
While these components form the core of the typical
platform for environmental sensing, the value proposition
is greatly reduced by the remaining cost components:
transducers, housing, and deployment. Compared with cell
phones or television remotes, environmental sensing is a
miniscule market. As a result, the current cost of trans-
ducers and the housing usually dwarfs the cost of the com-
putational and communications elements of a WSN node.
Until some step change in tranducer technology occurs,
widespread environmental monitoring with hundreds or
thousands of nodes will not be economically feasible, apart
from a few areas of extreme scientific interest. Areas of
increasing global interest such as improved understanding
around greenhouse gas emissions and carbon sequestration
will be likely areas that could see increased investment in
large-scale long-term monitoring initiatives. Likewise the
increasing scarcity of water resources in many regions
could well see increased investment in innovative water
monitoring and management practices which utilize sensor
networks. This could in turn bring about a new generation
of transducer technology, reducing current costs by orders
of magnitude.
C. Alternative Technologies
While large-scale WSNs offer some clear potential for
improved environmental sensing into the future, there are
competitive technologies which will also improve over the
next 5–10 years. Satellite remote sensing is already used
extensively to infer a lot of information about the planet.
Using systems such as SPOT-5, the technology already
exists to extract multispectral features at resolutions better
than 10 m2Valbeit at a significant cost.
Increased demand for connectivity in rural and remote
regions should also see an increased spread of 3G, 802.11,
and 802.16 coverage into regions where this has never
been previously available. At the moment, the power con-
sumption of 3G and 802.11 class devices is too high to be
practical for most long-term environmental monitoring
applications, however new generations of low-power
802.11 and 3G radios are expected to grow in the market,
making these potentially viable for applications where
multihop wireless nodes are currently the only option.
In many ways, only time will tell which technologies
will become the preferred options for specific environ-
mental sensing applications. It is likely that a combination
of the technologies available today will be utilized for fu-
ture deployment scenarios. Growing focus around the use
of cognitive radios opens up new opportunities for current
Bmote-class[networks to merge with other wireless ser-
vices in order for systems to overcome some of the current
limitationsaroundunreliablelinks,ortheneedforshort
periods of high network capacity.
V. THE ROAD AHEAD
Over the years we have deployed sensor networks for a
wide range of applications, mostly to monitor outdoor en-
vironments that require long-range operation for prolonged
periods without maintenance. We are currently at a stage of
high confidence and stability in deploying these types of
networks and tailoring the technology for each application.
This stability suggests a fork in the road for environmental
sensor networks: 1) either the technology is mature enough
for larger scale adoption by scientists, farmers, and the
wider industry; or 2) the technology is ready for a next
phase of research and development of more advanced
functionality to meet the demands of the end users. It is
most likely that a combination of commercialization and
development activities will characterize the future direc-
tion for this technology.
Further R&D in sensor networks could represent a shift
in data flow, storage, and communication. Sensor net-
works have been so far treated as data gathering tools.
While the initial wave of sensor network deployments has
focused on periodic sample-and-send applications, the
next wave will rely increasingly on performing in-network
processing for a higher degree of adaptability to dynamic
physical environments. Technological improvements in
processing capacity and form factor will enable nodes to
Corke et al.: Environmental Wireless Sensor Networks
1914 Proceedings of the IEEE |Vol.98,No.11,November2010
decide on-the-fly whether to process, compress, store, act
on, or send sensor data back to users. Such decisions will
depend on the fusion of multiple streams of information,
including data from different transducers, channel state,
local energy state, and application-specific user policy.
To drive such decisions, sensor nodes will process data
locally, fusing readings from multiple transducers with
current radio and energy states to transform transducer
data into more useful information. There is also a shift
from the use of simple scalar transducers, such as tem-
perature, humidity, and light, to more complex multime-
dia transducers, such as audio, image, and video sensors.
The increased sensing modality of sensor nodes also poses
new challenges, such as the design of appropriate trig-
gering strategies among various transducers and the
distributed coordination among multiple nodes in a
heterogeneous sensor networks.
Anothertimelyissueisthatofstandardization.Aswith
other maturing technologies, sensor networks have
reached a stage where standard protocols are consolidating
diverse research proposals that exist. IEEE 802.15.4 and
6loWPAN lay the infrastructure for future networks with
IPv6 support. Building on these standards, and to support
the wide range of emerging applications, we are in the
process of building the next generation of sensor nodes by
moving towards software-defined radio, with support for
frequency-, antenna-, modulation-, and data-rate diversity.
We expect these new nodes to provide WSNs with a more
versatile and robust communication architecture on which
to build services for the diverse application space. The shift
towards IP support enables simple web servers to run on
sensor nodes, so that users can access individual trans-
ducer data through standard web browsers. This may drive
a shift in data storage and reporting models for WSNs,
where portions of data may reside on the node, in raw or
compressed form, for provision to the user only upon
request.
The increasing integration of sensor networks with the
internet, through IP support, raises new security chal-
lenges. While physical security challenges, such as vanda-
lism to sensor nodes in our lake network, will always exist,
mechanisms for securing communication, processing, and
storage of data at sensor nodes will need to be addressed at
every layer in the communication stack. Efforts in this
direction, such as the TPM [44] and secure wireless key
exchange, have already started, but more work is needed
for an integrated cross-layer approach to the security issue.
Theissueofnetworkscalewillbeoneofthenextbig
hurdles for our development and deployment work. While
newer radios, processors, and communication protocol
pave the way for larger networks, scaling up to deploy-
ments with hundreds of nodes that run for months or
years will require some form of hierarchy in the network.
Another major challenge for scaling up is detecting node
malfunctions, which become more probable, and recov-
ering from these faults seamlessly. We expect that
development of effective network programming models
will also increase in importance for larger deployments, as
current model for per-nodeprogrammingwillnot
scale well.
While our node software and hardware development
processes have reached a mature stage that allows nonspe-
cialists to deploy nodes, there remains a need for defining
more streamlined deployment models to ensure wider up-
take of the technology. Easily configurable software, such
as our ToSense application, and hardware, such as the
stackable daughterboard are key components of this model.
Thenextstepistoensurethattheprocessofcomposing
working systems from these components is easy for
nonspecialists.
In summary, the next wave in environmental and
agricultural sensor networks will combine commercializa-
tion of current technology and development of more
advanced functionality. This will include nodes with
multiple sensing modalities and diverse radio configura-
tions, as well as the continuous redefinition of the design
space to identify and address challenges that emerge in this
application space, and indeed in transferring lessons
learned to new applications as well. Adaptive power man-
agement strategies that efficiently manage these activities
without compromising performance quality also remain an
open direction for continued investigation. h
Acknowledgment
Theauthorswouldliketothanktheircolleagues:
P.Sikka,G.Winstanley,S.Brosnan,L.Overs,J.Whitham,
G.Foley,C.Crossman,B.Mackey,D.Swain,G.Bishop-
Hurley,M.Bruenig,S.Sen,P.Flick,B.Wood,D.Palmer,
G. Salagnac, L. Klingbeil, C. Richter, D. O’Rourke,
M. D’Souza, M. Dunbabin,R.Dungavell,D.Hugo,
P. McCarthy, C. Knight, K. Tane, and Powercom Ptd Ltd;
andtheirstudents:S.Rothery,P.Schmid,V.Ho,M.Ung,
T. Wen-Chan, and N. Nourani-Vatani.
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Corke et al.: Environmental Wireless Sensor Networks
1916 Proceedings of the IEEE | Vol. 98, No. 11, November 2010
ABOUT THE AUTHORS
Peter Corke (Fellow, IEEE) received the Ph.D.
degree in robotics from the University of
Melbourne, Melbourne, Vic., Australia, in 1995.
Currently, he is a Professor at the School of
Engineering Systems, Queensland University of
Technology, Brisbane, Qld., Australia. Previously,
he was the founding Research Director of the
Autonomous Systems Laboratory, CSIRO ICT Cen-
tre, Brisbane, Qld., Australia. His research activi-
ties span machine vision, vision-based robot
control, field robotics, and sensor networks.
Dr. Corke is the Editor-in-Chief of the IEEE ROBOTICS AND AUTOMATION
MAGAZINE, a member of the editorial board of the International Journal of
Robotics Research and a founding editor of the Journal of Field Robotics.
Tim Wark (Member, IEEE) received the Ph.D.
degree in multimodal signal processing and pat-
tern recognition from the Queensland University
of Technology, Brisbane, Qld., Australia, in 2000.
Currently, he is a Principal Research Scientist at
the Autonomous Systems Laboratory, CSIRO ICT
Centre,Brisbane,Qld.,Australia.Hiscurrent
research interests are in in-network processing
in wireless sensor networks, with a particular
focus on the applications in the environmental
domain.
Raja Jurdak (Member, IEEE) received the Ph.D.
degree in ad hoc and sensor networks from the
University of California, Irvine, in 2005.
He has been a Principal Research Scientist at
the Autonomous Systems Laboratory, CSIRO ICT
Centre,Brisbane,Qld.,Australia, since October
2008, where he currently leads the Sensor Net-
works Research Team. He has over 40 peer-
reviewed publications, as well as a book Wireless
Ad Hoc and Sensor Networks: A Cross-Layer
Design Perspective (New York: Springer-Verlag, 2007). His current
research interests are modeling, optimization, and real-world deploy-
ments of energy-efficient and highly responsive sensor networks.
Wen Hu (Member, IEEE) received the Ph.D. degree
in sensor networks from the University of New
South Wales, Sydney, N.S.W., Australia, in 2006.
Currently, he is a Researcher at the Autono-
mous Systems Laboratory, CSIRO ICT Centre,
Brisbane, Qld., Australia. His current research
interests are low-power communications, com-
pressive sensing, and security issues in sensor
networks.
Philip Valencia (Member, IEEE) received a double
bachelors degree in engineering and computer
science from the Queensland University of
Technology, Brisbane, Qld., Australia, in 2001,
where he is currently working towards the Ph.D.
degree with a research focus of distributed online
learning for wireless sensor and actuation net-
works.
Currently, he is a Research Engineer at the
Autonomous Systems Laboratory, CSIRO ICT Cen-
tre, Brisbane, Qld., Australia, with eight years experience of program-
ming motes and wireless sensor network deployments.
Darren Moore (Member, IEEE) received the
M.Eng. degree in microphone array processing
from the Queensland University of Technology,
Brisbane, Qld., Australia.
Currently, he is a Research Engineer at the
Autonomous Systems Laboratory, CSIRO ICT Cen-
tre, Brisbane, Qld., Australia. His current research
interests are in acoustic applic ations of w ireless
sensor networks for environmental monitoring.
Corke et al.: Environmental Wireless Sensor Networks
Vol. 98, No. 11, November 2010 | Proceedings of the IEEE 1917
... In recent ten years since Kaul et al. proposed a framework to quantify the timeliness of information in 2012 [1], one of the most popular ideas in timely update system design has been how to keep information as fresh as possible and ensure timely information delivery. For timely update systems such as vehicle networks where the vehicle's velocity and location are disseminated to ensure safe transportation [2], environmental sensor networks where the updates of a time-varying phenomenon are collected for largescale monitoring [3], and wireless communication networks where adaptive scheduling algorithms are adopted based on the time-varying channel state information [4], achieving timely delivery can freshen the monitor's awareness of the sources and thus assist correct and efficient decision making. ...
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... The availability of inexpensive low power microcontrollers, sensors, and transceivers in the late 1990s resulted in a flurry of wireless sensor network (WSN) activity in the early 2000s [1]. By the mid-2000s, WSN experts (i.e., computer and software engineers) were collaborating with geoscientists to deploy WSNs for environmental monitoring [2][3][4]. ...
... However, these efforts largely focus on wireless technology itself (e.g., protocols, specification, etc.). In 2018, Jamali-Rad and Campman [38] proposed a wireless sensing framework, that utilized low-power widearea network (LPWAN), to prioritize: (1) inherently IoT-compatible, low power, and long range wireless sensors; (2) scalable advanced wireless networking protocols; and (3) cloud storage and computing. The static context header compression initiative (SCHC) for IoT interoperability further strengthens the viability of LPWAN of IoT applications [39]. ...
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... Network autoregressions allows meaningful analysis of the actual wind speed, for each node, based on the effect of past speeds and the velocity measured on its neighbor stations; see Sect. 4. This methodology is potentially useful to model sensor networks for environmental monitoring. See [6,8,22,25], among others, who discuss application of wireless sensor network for environmental, agricultural and intelligent home automation systems. See also [41] for an application to social network analysis. ...
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We discuss a unified framework for the statistical analysis of streaming data obtained by networks with a known neighborhood structure. In particular, we deal with autoregressive models that make explicit the dependence of current observations to their past values and the values of their respective neighborhoods. We consider the case of both continuous and count responses measured over time for each node of a known network. We discuss least squares and quasi maximum likelihood inference. Both methods provide estimators with good properties. In particular, we show that consistent and asymptotically normal estimators of the model parameters, under this high-dimensional data generating process, are obtained after optimizing a criterion function. The methodology is illustrated by applying it to wind speed observed over different weather stations of England and Wales.
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